Multivariate simulation‐based forecasting for intraday power markets: Modeling cross‐product price effects.
| Title: | Multivariate simulation‐based forecasting for intraday power markets: Modeling cross‐product price effects. |
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| Authors: | Hirsch, Simon1,2 (AUTHOR) simon.hirsch@statkraft.com, Ziel, Florian2 (AUTHOR) |
| Source: | Applied Stochastic Models in Business & Industry. Nov2024, Vol. 40 Issue 6, p1571-1595. 25p. |
| Subject Terms: | *Electricity markets, *Renewable energy sources, *Electricity pricing, *Market design & structure (Economics), *Prediction markets, Marginal distributions |
| Abstract: | Intraday electricity markets play an increasingly important role in balancing the intermittent generation of renewable energy resources, which creates a need for accurate probabilistic price forecasts. However, research to date has focused on univariate approaches, while in many European intraday electricity markets all delivery periods are traded in parallel. Thus, the dependency structure between different traded products and the corresponding cross‐product effects cannot be ignored. We aim to fill this gap in the literature by using copulas to model the high‐dimensional intraday price return vector. We model the marginal distribution as a zero‐inflated Johnson's SU$$ {S}_U $$ distribution with location, scale, and shape parameters that depend on market and fundamental data. The dependence structure is modeled using copulas, accounting for the particular market structure of the intraday electricity market, such as overlapping but independent trading sessions for different delivery days and allowing the dependence parameter to be time‐varying. We validate our approach in a simulation study for the German intraday electricity market and find that modeling the dependence structure improves the forecasting performance. Additionally, we shed light on the impact of the single intraday coupling on the trading activity and price distribution and interpret our results in light of the market efficiency hypothesis. The approach is directly applicable to other European electricity markets. [ABSTRACT FROM AUTHOR] |
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| Items | – Name: Title Label: Title Group: Ti Data: Multivariate simulation‐based forecasting for intraday power markets: Modeling cross‐product price effects. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hirsch%2C+Simon%22">Hirsch, Simon</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> simon.hirsch@statkraft.com</i><br /><searchLink fieldCode="AR" term="%22Ziel%2C+Florian%22">Ziel, Florian</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Applied+Stochastic+Models+in+Business+%26+Industry%22">Applied Stochastic Models in Business & Industry</searchLink>. Nov2024, Vol. 40 Issue 6, p1571-1595. 25p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Electricity+markets%22">Electricity markets</searchLink><br />*<searchLink fieldCode="DE" term="%22Renewable+energy+sources%22">Renewable energy sources</searchLink><br />*<searchLink fieldCode="DE" term="%22Electricity+pricing%22">Electricity pricing</searchLink><br />*<searchLink fieldCode="DE" term="%22Market+design+%26+structure+%28Economics%29%22">Market design & structure (Economics)</searchLink><br />*<searchLink fieldCode="DE" term="%22Prediction+markets%22">Prediction markets</searchLink><br /><searchLink fieldCode="DE" term="%22Marginal+distributions%22">Marginal distributions</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Intraday electricity markets play an increasingly important role in balancing the intermittent generation of renewable energy resources, which creates a need for accurate probabilistic price forecasts. However, research to date has focused on univariate approaches, while in many European intraday electricity markets all delivery periods are traded in parallel. Thus, the dependency structure between different traded products and the corresponding cross‐product effects cannot be ignored. We aim to fill this gap in the literature by using copulas to model the high‐dimensional intraday price return vector. We model the marginal distribution as a zero‐inflated Johnson's SU$$ {S}_U $$ distribution with location, scale, and shape parameters that depend on market and fundamental data. The dependence structure is modeled using copulas, accounting for the particular market structure of the intraday electricity market, such as overlapping but independent trading sessions for different delivery days and allowing the dependence parameter to be time‐varying. We validate our approach in a simulation study for the German intraday electricity market and find that modeling the dependence structure improves the forecasting performance. Additionally, we shed light on the impact of the single intraday coupling on the trading activity and price distribution and interpret our results in light of the market efficiency hypothesis. The approach is directly applicable to other European electricity markets. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Applied Stochastic Models in Business & Industry is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/asmb.2837 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 1571 Subjects: – SubjectFull: Electricity markets Type: general – SubjectFull: Renewable energy sources Type: general – SubjectFull: Electricity pricing Type: general – SubjectFull: Market design & structure (Economics) Type: general – SubjectFull: Prediction markets Type: general – SubjectFull: Marginal distributions Type: general Titles: – TitleFull: Multivariate simulation‐based forecasting for intraday power markets: Modeling cross‐product price effects. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hirsch, Simon – PersonEntity: Name: NameFull: Ziel, Florian IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 15241904 Numbering: – Type: volume Value: 40 – Type: issue Value: 6 Titles: – TitleFull: Applied Stochastic Models in Business & Industry Type: main |
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